Categorization: Defining Features and Family Resemblances

1. Start part 4 – Next slide is carry-over from part 3

2. Explanations of how we categorize have fallen into three broad perspectives: Defining features Family resemblances Theory theories

3. Category membership via definitions -- Aristotle until the 1950's  "Classical approach" -- based on defining features     Categories are defined by a set of necessary and sufficient features.      We develop rules that define categories based on the set of features.      This approach dominated Western thinking re categorization for over 2000 years.      It's strongly appealing for several reasons:              It clearly explains how we decide whether an object belongs to a category                  --objects with the nec and suff features belong to the category,                  --objects without those features are not members of the category.              Its explanation of category formation is intuitively appealing:                    We form categories by encountering many objects, and discover the   "nec & suff" features that divide the objects into separate classes.    An object has to have all of the defining features in order to be in the category           E.g., sorting cards into similar groups:  black/spades -- red/hearts -- etc    Definitions are usually arbitrary, used for convenience especially among experts    Definitions work well for highly structured & constrained categories          E.g., mathematical constructs, geometry

4. Problems: People usually unable to identify the defs they use for most categories   Defs often vary across people for the same category, & for the same person over time   How can we communicate if our notions of categ’s are based on inconsistent definitions?   Empirical research to test the definitions approach has not supported it       E.g., since "male" is part of the def of "bachelor," it's a simpler construct than is bachelor.        Thus, we'd expect RTs in categorization tasks to "male" to be faster than to "bachelor."        But that turned out to be untrue -- response times are about the same.     For “female” we have a more complicated problem: Single adult male  bachelor Single adult female  ?? Spinster? Maiden? Bachelorette?! Categorization research reveals "typicality" effects, which are not rule-based (Rosch, 1973)     E.g., "robin" is more typical of the category "birds" than is "emu," though both are birds.     Such effects are very strong & persistent across many research projects.     If our categorizations were based on a set of defining rules, typicality effects should not occur. Definitions don't work so well for "natural" and "everyday" categories     They are "fuzzy" in the sense that it's hard to establish clear, defining rules for all instances of a category.      That is, some items might be either in category A or else in category B

5. Game . . .

6. 1. an amusement or pastime: a children's game. 2. the material or equipment used in playing certain games: a store selling toys and games. 3. a competitive activity involving skill, chance, or endurance, played according to a set of rules, usually for the players own amusement or for that of spectators. 4. a single occasion of such an activity, or a definite portion of one: the final game of the season; a rubber of three games at bridge. 5. the number of points required to win a game. 6. the score at a particular stage in a game: With five minutes to play, the game was 7 to 0. 7. a particular manner or style of playing a game: Her game of chess is improving. 8. anything resembling a game, as in requiring skill, endurance, or adherence to rules: the game of diplomacy. 9. a trick or strategy: to see through someone's game. 10. fun; sport of any kind; joke: That's about enough of your games. 11. wild animals, including birds and fishes, such as are hunted for food or taken for sport 12. the flesh of such wild animals or other game, used as food: a dish of game. 13. any object of pursuit, attack, abuse, etc.: The new boy at school seemed to be fair game for practical jokers. 14. a business or profession: He's in the real-estate game. 15. having the required spirit or will: Who's game for a hike through the woods? 16. to act in an evasive, deceitful, manipulative, or trifling manner in dealing with others: Don't play games with me—I want to know if you love me or not! 17. to act honorably or justly: We assumed that our allies would continue to play the game. 18. lame: a game leg.

7. Wittgenstein:  Those dreadful, uncooperative games: how do we define “game?” The elements of games, such as play, rules, and competition, all fail to adequately define what games are. Wittgenstein concluded that people apply the term game to a range of disparate human activities that bear to one another only what one might call family resemblances. - Wikipedia

8. Categories via "family resemblance" -- two approaches Prototypes versus Exemplars

9. Prototypes -- Eleanor Rosch & Carolyn Mervis (1975), and many others Two major ideas: 1. We store an “average” image of a class of objects     The “average” is an abstraction      It may not actually exist in the natural world          C.f., Plato's "ideal forms“     Category boundaries are expected to be "fuzzy," not sharply defined     Categories share some characteristic properties, attributes, or features     A particular object doesn't have to have all the common properties to be in the category Recognition occurs when we match an object to a prototype Similar to template theory, but more parsimonious:      doesn’t require a vast # of individual items in memory      more flexible, since the prototype is readily updated with new experiences

10. 2. category membership is organized hierarchically “Basic Level” = level that has the highest degree of cue salience E.g., if asked, “What are you sitting on?” A person is more likely to say “Chair,” than to say “Furniture.” Regarding animals, basic categories might be dog, bird, or fish. Basic categories have high informational content E.g., they can easily be categorized based on visual gestalt information -- we can readily visualize a “typical” dog, for example, or a “typical” fish, but not so readily visualize a “typical” animal Note that basic categories in everyday human experience often don’t match scientific categories: E.g., “dog” refers to a specific scientific species (Canis lupus familiaris ), whereas “bird” or “fish” refer to large scientific groups that include many different species.

11. Empirical research support: Prototypical objects = highly similar to the prototype Prototypical objects = quick & easy recognition    Objects not very similar to prototype = slow & uncertain recognition Prototypical objects = named first in a listing task Prototypical objects = affected more by priming than are non-prototypical objects Prototypes vary with personal experience as well as across different cultures       E.g., your idea of a prototypical bird might differ from mine

12. Strengths:    accounts for many "everyday" and "natural" categories     accounts for "typicality" effects     accounts for differing notions of category membership, across people &/or across time     much empirical research supports this approach Problems – its strength reveals its weakness:   Going for “average” means individual features are often discarded     Thus misrecognition can occur when a specific object is not very similar to the prototype Communication difficulties arise when category inclusion differs for diff people   

13. Exemplars -- Robert Nosofsky (1991) & others Whereas prototypes are abstract "ideals“ Exemplars are real members of a category.    These exemplars are learned from personal experience    They are stored in memory & are used for comparisons with new objects.     A new object is compared to one or more exemplars of a category.

14. Strengths:             Same as those for prototype approach:   accounts for many "everyday" and "natural" categories      accounts for "typicality" effects     accounts for differing notions of category membership, across people &/or across time      much empirical research supports this approach Problems:     Similar to prototypes, except discarding individual feats is not a problem Misrecognitions arise when exemplars are inadequate to determine the category           especially for objects from the "fuzzy" boundary of the category     Unclear how categories are originally formed That is, when I encounter something utterly new, how do I know how to categorize it? Maybe we have a catch-all “weird stuff” category until we have additional experiences with instances of the new something, then memory for the previous instances takes over, using one of them as the exemplar . . .     Risks requiring large # of exemplars to account for all instances of a category

15. Combining/comparing prototypes & exemplars “We know generally what cats are (the prototype), but we know specifically our own cat is the best (an exemplar).”                                                                               -- Minda & Smith (2001) Some theorists argue that we use both approaches:     Exemplars . . . better for tasks involving smaller-sized categories     Prototypes . . . better for tasks involving larger-sized categories     Exemplars . . . better account for recognizing atypical instances of a category As we might divine from the foregoing, similarity theories of categorization have run into a sort of "dead end." They are pretty good at accounting for much of categorization effects, but cannot be readily distinguished in terms of their strengths & weaknesses. Further, recent theorizing takes the position that thinking – concepts – categories are all considerably more complex than the foregoing theories suggest.

16. Recent approaches – “Theory theories” – or – Expertise theories – Douglas Medin (1990's, 2000’s) & many others “It's less important to try to cover a whole range of facts than it is to introduce students to a few big ideas.” The earlier approaches tended to focus on classification, and features They also assume that categories are (1) natural and hence can be studied by ordinary empirical science (2) based on a particular model -- defining features, exemplars, or prototypes Earlier approaches also emphasized objects, thus ignored relations, actions, etc For Medin, personal & cultural ideas about how things work, their relations, etc, are the primary issues in categorization. For example, the earlier approaches might explain how “fruit” is categorized by referring to a list of features that define fruits; or by resemblances among fruits. A role/relational explanation might focus on what we do with fruits, their effects on our health, their role in disseminating seeds, etc.  

17. Medin uses a sample question to illustrate the effect of expertise: “Suppose there's one disease that white pine and weeping willow get and another disease that river birch and paper birch get. Which disease do you think is more likely to affect all trees?” . . .

18. The findings from the preceding and other studies led him to conclude that most of the findings published about categorization and reasoning were only valid for “novices” with no specialized knowledge. “We are currently developing and testing theories that we hope will extend beyond the typical undergraduate subject pool to other populations. Our work shows patterns that systematically diverge from data collected from undergraduates, suggesting that [categorization] theory and data based on undergraduates may not generalize to the world at large.” Medin profile & source for the foregoing excerpts: http://www.pnas.org/content/104/48/18883.full

19. Summary Categorization is turning out to be far more complex than we had thought: Sometimes the classical defining features work best, especially for highly constrained, formal categories. Sometimes prototypes work best, especially for large, fuzzy categories. Sometimes exemplars work best, especially for small, fuzzy categories and for identifying atypical instances. Sometimes coherence, roles, relations, etc work best, especially for categories where multidimensional and dynamic qualities are important.